{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import tensorflow as tf\n",
    "from sklearn import datasets\n",
    "from sklearn.model_selection import train_test_split\n",
    "from tensorflow.keras import Model, layers, activations\n",
    "\n",
    "from plot_utils import plot_cost_accuracy, plot_decision_boundary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "X, y = datasets.make_blobs(n_samples=100000, n_features=2, centers=3, random_state=0)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "X_val, X_test, y_val, y_test = train_test_split(X_test, y_test, test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((80000, 2), (80000,), (10000, 2), (10000,), (10000, 2), (10000,))"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape, y_train.shape, X_val.shape, y_val.shape, X_test.shape, y_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x1a3c9e6afc8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(X_train[:1000, 0], X_train[:1000, 1], s=40, c=y_train[:1000], cmap=plt.cm.Spectral)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_data = tf.data.Dataset.from_tensor_slices((X_train, y_train)).shuffle(80000, reshuffle_each_iteration=True).batch(32)\n",
    "val_data = tf.data.Dataset.from_tensor_slices((X_val, y_val)).batch(32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "class MLP(Model):\n",
    "    def __init__(self, num_classes=3):\n",
    "        super(MLP, self).__init__()\n",
    "        self.hidden_layer = layers.Dense(3, activation=activations.relu)\n",
    "        self.output_layer = layers.Dense(num_classes, activation=activations.softmax)\n",
    "\n",
    "    def call(self, inputs, training=None, **kwargs):\n",
    "        x = self.hidden_layer(inputs)\n",
    "        x = self.output_layer(x)\n",
    "        return x\n",
    "\n",
    "\n",
    "@tf.function\n",
    "def _train_step(x, y, model, optimizer, loss_object, train_loss, train_accuracy):\n",
    "    with tf.GradientTape() as tape:\n",
    "        y_pred = model(x, training=True)\n",
    "        # print(\"=> label shape: \", y.shape, \"pred shape\", y_pred.shape)\n",
    "        loss = loss_object(y, y_pred)\n",
    "    gradients = tape.gradient(loss, model.trainable_variables)\n",
    "    optimizer.apply_gradients(zip(gradients, model.trainable_variables))\n",
    "    train_loss(loss)\n",
    "    train_accuracy(y, y_pred)\n",
    "\n",
    "\n",
    "@tf.function\n",
    "def _val_step(x, y, model, val_accuracy):\n",
    "    y_pred = model(x)\n",
    "    val_accuracy(y, y_pred)\n",
    "\n",
    "\n",
    "def build_model(train_data, val_data, learning_rate=0.001, epochs=100, print_cost=False):\n",
    "    # keep track of cost\n",
    "    costs = []\n",
    "    train_accs = []\n",
    "    val_accs = []\n",
    "\n",
    "    model = MLP()\n",
    "    # Stochastic gradient descent optimizer.\n",
    "    optimizer = tf.optimizers.SGD(learning_rate)\n",
    "\n",
    "    loss_object = tf.keras.losses.SparseCategoricalCrossentropy()\n",
    "    train_loss = tf.keras.metrics.Mean(name='train_loss', dtype=tf.float32)\n",
    "    train_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='train_accuracy')\n",
    "    val_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='val_accuracy')\n",
    "\n",
    "    for i in range(epochs):\n",
    "        for x_, y_ in train_data:\n",
    "            _train_step(x_, y_, model, optimizer, loss_object, train_loss, train_accuracy)\n",
    "\n",
    "        for x_, y_ in val_data:\n",
    "            _val_step(x_, y_, model, val_accuracy)\n",
    "\n",
    "        if print_cost and (i + 1) % 10 == 0:\n",
    "            template = \"Cost after iteration {}: {}, Accuracy on train data: {}, Accuracy on validation data: {}\"\n",
    "            print(template.format(i + 1, train_loss.result(), train_accuracy.result(), val_accuracy.result()))\n",
    "\n",
    "        costs.append(train_loss.result())\n",
    "        train_accs.append(train_accuracy.result())\n",
    "        val_accs.append(val_accuracy.result())\n",
    "    \n",
    "    return model, costs, train_accs, val_accs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:Layer mlp is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2.  The layer has dtype float32 because it's dtype defaults to floatx.\n",
      "\n",
      "If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.\n",
      "\n",
      "To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.\n",
      "\n",
      "Cost after iteration 10: 0.33102741837501526, Accuracy on train data: 0.8697712421417236, Accuracy on validation data: 0.8875200152397156\n",
      "Cost after iteration 20: 0.2768542766571045, Accuracy on train data: 0.8921831250190735, Accuracy on validation data: 0.9021700024604797\n",
      "Cost after iteration 30: 0.2561015784740448, Accuracy on train data: 0.9006283283233643, Accuracy on validation data: 0.9077433347702026\n",
      "Cost after iteration 40: 0.2451898753643036, Accuracy on train data: 0.9050509333610535, Accuracy on validation data: 0.9108625054359436\n",
      "Cost after iteration 50: 0.23852410912513733, Accuracy on train data: 0.9077552556991577, Accuracy on validation data: 0.9128260016441345\n",
      "Cost after iteration 60: 0.23404911160469055, Accuracy on train data: 0.9095764756202698, Accuracy on validation data: 0.9142150282859802\n",
      "Cost after iteration 70: 0.2308381199836731, Accuracy on train data: 0.9108789563179016, Accuracy on validation data: 0.9152185916900635\n",
      "Cost after iteration 80: 0.22842848300933838, Accuracy on train data: 0.9118595123291016, Accuracy on validation data: 0.9159887433052063\n",
      "Cost after iteration 90: 0.2265492081642151, Accuracy on train data: 0.9126301407814026, Accuracy on validation data: 0.9165688753128052\n",
      "Cost after iteration 100: 0.22504431009292603, Accuracy on train data: 0.9132411479949951, Accuracy on validation data: 0.917041003704071\n"
     ]
    }
   ],
   "source": [
    "model_trained, costs, train_accs, val_accs = build_model(train_data, val_data, learning_rate=0.001, epochs=100, print_cost=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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e5r4UJi2AySdB8wnBSPIIjML2pLKs29nF2h3BsW5HF+t3dbNxVzdbO3r3WbosFjGm1Fcyrb6K02Y3MLW+iqn1lUyuqxx4nViTIBYd/vrCIuWoPBLovBpoEZEyNx3YmHfdApw5qM8K4A3AH83sDGA2MGOYz2Jm1wDXAMyaNWvEAi85u9bCmgdgze9h3YN7J/LVToVZZ8PMD8DMM4KEOVZx1B/X1ZfhuW17eH5bZ/C6vZMXWjvZ1N6zT5I8qbaC2U1Jzp7fxKzGJDMbksxsTDKjoYrJdZUaMRYZAWWRQNeoBlpEpN9Q2dPg4tYvA183s+XAk8DjQGaYz+LutwC3ACxevHgUt24osGw6GGF+Zgms/jXseiFor50Gx14Ic86F2S8JapePcmS5dU8fT23ezdObdvPUpg6e2drB+l3dA4lyRSzC/OYaTp3VwJtOm8m85mrmNVczp6laq02JjIKy+K9soAZaI9AiIi3AzLzrGcDm/A7u3gFcDWDBTK+14ZE81LPjTi4H6/8IK/4Xnv15MAkwVhWUY5z5NzD/Amiaf1QJc18my9ObO3hsfRuPb2hn+cZ2NrX3DNyf05TkxKl1vOHUGRw/pZbjJ9cyszGpkWSRIiqLBDrZXwOtSYQiIkuBY81sLrAJuAJ4a34HM5sAdLt7Cngv8Ad37zCzQz47bnRsgcf+G5b/IFh/uaIOjn8NnHgJzH9FsEveEepNZ3l8QzsPr9nJn9fsZPnGdlLhRh8zGqpYNGsC7zpnDifPqOekaXXUVo7MUnQiMnLKIoGORoyqeFQj0CJS9tw9Y2bXAfcRLEV3q7s/bWbXhvdvBk4E/tvMsgQTBN9zsGeL8XMUzMZH4M/fglU/hVwG5r0cXvEZOPG1EK864rd9obWTB57ZzoPP7+Ava3fSm84RMThpWj1vP2s2i2c3cNqcBibVVo7YjyIihVMWCTQEEwk7VQMtIoK7LwGWDGq7Oe/8YeDY4T47Lmz4M/zuS7Dmd8GW1WdeC6e/BxrnHdHb5XLOYxva+OVTW/nNM9tZu6MLgPnN1Vxx+ixecsxEzpjbSH2VRpdFxqKySaBrKjQCLSIig+x8Ae77JDz3y2Dnvgv/GRa/+4g2KnF3Ht/Yzj2Pb+IXT21l+54+EtEIZ89v4uqXzOEVJ0xiRsORl36ISOkomwS6uiKmBFpERALpHvjD9fDQNyGagFd+Ds645ogS5627e/nRso38+LEW1u/spiIW4fzjJ3HxyVN4xQmTVMMsMg6VVwKtSYQiIrLlCfjxe4ONT170Fnjl56Fu6mG9hbvz4PM7+O+H1/PbZ7aRczhnfhPXnX8MFy2coqRZZJwrnwQ6EWVHZ6rYYYiISLG4w8M3wq8/D8kmuOouOOaCw3qLVCbHT5Zv4jsPruXZbXuYWJPgb86bz5Wnz2JWk8ozRMpF+STQFTHW7+wudhgiIlIMmT645zp48kdwwmvhkm9AddOwH09lctz5aAs3PbCaTe09nDCllq++6RQuOWUqFbFoAQMXkVJUNgl0TUVMW3mLiJSjrp3wv2+DDQ/DBZ+Bcz887I1P3J2fPrGFr/ziGTa197Bo5gS++PqFnHdcM3aUuw2KyNhVNgm0JhGKiJShji3wvb+C3S1w+Xdh4RuG/eiKje38089W8uj6NhZMrVPiLCIDyiqB7k5nyeWciLY/FREZ//Zsg+9fAp3b4J33wqyzhvVYdyrDV+97ju8+tJam6gr+9Y0v4o2nzdDW2SIyoHwS6EQUd+hJZ6muKJsfW0SkPHXtgP9+HXRsCiYLDjN5/vOanXzszifYsKubt581m49ddLxW1BCR/ZRNJtmfNHf1ZZRAi4iMZ5kU/PAt0LYe3vZ/MPvsQz6Syzk3PrCaG379HLMak/zvNWdx5rzhTzIUkfJSNplkTZg0d/ZlmFTkWEREpIB++0+waRm86fsw96WH7L6rK8WH7nicB5/fwWWLpvHF15+sgRYROaiy+YbYOwKdLXIkIiJSMM/dF+wuePp74aTLDtl9/c4u3nnrI2ze3cuX3nAyV5w+U5MEReSQIoV8czO7yMyeNbPVZvbxIe5/1MyWh8dTZpY1s8ZCxFKdCNbp1G6EIiLj1O5NcPffwJST4cIvHrL7io3tvOFbD7G7J83tf30WV54xS8mziAxLwRJoM4sCNwEXAwuAK81sQX4fd7/e3Re5+yLgE8Dv3X1XIeLJr4EWEZFx6JcfDzZMufx7EK88aNeHX9jJFbf8mapElDvfdw6nzW4YnRhFZFwo5Aj0GcBqd1/j7ingDuDSg/S/Eri9UMFU59VAi4jIOLNxKay6F17yIZh4zEG7Pr6hjfd+fykzGqq4633nML+5ZpSCFJHxopAJ9HRgY951S9i2HzNLAhcBPy5UMDWqgRYRGZ/c4defhepmOPu6g3ZdtaWDd313KU01FfzgvWcyqe7gI9UiIkMpZAI9VCGZH6DvJcCfDlS+YWbXmNkyM1vW2tp6RMFUV4Q10BqBFhEZX56/H9b/Cc77R6g48GjypvYe3v6dR6iKR7ntvWcyWcmziByhQibQLcDMvOsZwOYD9L2Cg5RvuPst7r7Y3Rc3NzcfUTDJRDgCrUmEIiLjRy4Lv/4cNM6D0951wG59mSx/e9tj9Kaz/OC9ZzCzMTlqIYrI+FPIBHopcKyZzTWzBEGSfO/gTmZWD5wH3FPAWIhGjKp4VCPQIiLjyaqfwvaV8IpPQ/TAOwZ+8eerWLGxnesvfxHHTKodxQBFZDwq2DrQ7p4xs+uA+4AocKu7P21m14b3bw67vh643927ChVLv+qKGJ2qgRYRGT8e/wHUTYcFB56jfs/yTfz3w+v565fO5eKTp45icCIyXhV0IxV3XwIsGdR286Dr7wHfK2Qc/WoqNAItIjJudGyGF34D534YItEhu2zZ3cMn73qS0+c08LGLThjlAEVkvCroRiqlproipgRaRGS8eOJ/wXOw6K0H7PL5e1eSyTlfe9Mi4tGy+itPRAqorL5NqhMxTSIUERkP3OHx22DW2dA0f8guv31mG798eisfvOBYZjVp0qCIjJzySqAroloHWkRkPGhZCjufh0VvG/J2TyrLZ+55mmMm1fDXL503ysGJyHhXZgm0SjhERMzsIjN71sxWm9nHh7hfb2Y/NbMVZva0mV2dd2+dmT1pZsvNbNnoRp7n8R9APAknXTbk7ZseWE1LWw9fvGwhiVhZ/VUnIqOgoJMIS01NRUxbeYtIWTOzKHAT8CqC9fqXmtm97r4yr9v7gZXufomZNQPPmtlt7p4K75/v7jtGN/I86V54+u5g5Y2K/Zeka+tKceuf1nLJKdM4c15TEQIUkfGurP5ZrhFoERHOAFa7+5owIb4DGLwGnAO1ZmZADbALKJ0vz5al0NdxwKXrvv/wOrpTWa47/5jRjUtEykZ5JdCJKN3pLLncgXYUFxEZ96YDG/OuW8K2fDcCJxLsHvsk8CF3z4X3HLjfzB41s2uG+gAzu8bMlpnZstbW1pGNHmD9Q4DBrLP2u9XVl+F7D63jlSdO5vgp2jBFRAqjvBLoihju0JPWREIRKVs2RNvgUYVXA8uBacAi4EYzqwvvvcTdTwUuBt5vZi/b783cb3H3xe6+uLm5eQRDD214CCafBFUN+926/ZENtHen+dvzh16ZQ0RkJJRdAg2ojENEylkLMDPvegbBSHO+q4G7PLAaWAucAODum8PX7cDdBCUhoyebho1Lg+XrBunLZPmvB9dw1rxGTp21f3ItIjJSyiqBrgkTaE0kFJEythQ41szmmlkCuAK4d1CfDcAFAGY2GTgeWGNm1WZWG7ZXAxcCT41a5ABbnoB0F8w+Z79bP3l8E9s6+vjbl6v2WUQKq6xW4dg7Aq0SDhEpT+6eMbPrgPuAKHCruz9tZteG928GvgB8z8yeJCj5+Ed332Fm84C7g7mFxIAfuvsvR/UH2PBQ8DpEAv3jRzcxv7malx47cVRDEpHyU14JdCIKoN0IRaSsufsSYMmgtpvzzjcTjC4Pfm4NcErBAzyY9Q9D4zyonbJP8+b2Hh5Zt4sPv+o4wgRfRKRgyqqEQzXQIiJjWC4XjEDP2n/0+WdPBGXcrztl2mhHJSJlqCwTaNVAi4iMQTuehZ42mL3/BMJ7V2zmRTPqmTOxugiBiUi5KasEukY10CIiY9f6oeuf17R28tSmDo0+i8ioKasEuroirIHWCLSIyNiz/iGomQINc/dp/umKLZjBa1+kBFpERkdZJdDJRDgCrUmEIiJjT8sjwe6DeZME3Z17V2zijDmNTKmvLGJwIlJOyiqBjkaMqnhUI9AiImNNJgW7W6D5+H2aV23ZwwutXbxukUafRWT0lFUCDVBbGaOjRwm0iMiY0rEJPAcTZu3T/MfVrQC86sTJxYhKRMpU2SXQDckEbd2pYochIiKHo31D8DoogV66ro05TUkm1al8Q0RGT9kl0BOScdq708UOQ0REDkd/Al0/c6Apl3OWrdvF6XMaixSUiJSrskugNQItIjIGtW8Ai0Dd9IGmF1o7aetOc/pcJdAiMrrKL4GujtOmEWgRkbFl90aonQaxxEDTI+t2AWgEWkRGXdkl0BOSCdq7U7h7sUMREZHhat+wX/3zsnVtTKypYE5TskhBiUi5KrsEuiEZJ5NzbectIjKWDJFAP7J2F2fMbcDy1oUWERkNZZdAT0gGv/7TREIRkTEimw6WsctLoDe397CpvYfFs1W+ISKjr6AJtJldZGbPmtlqM/v4Afq83MyWm9nTZvb7QsYDwSRCQBMJRUTGiiHWgF4a1j+foQmEIlIEsUK9sZlFgZuAVwEtwFIzu9fdV+b1mQB8C7jI3TeY2aRCxdOvsToOoImEIiJjxRBrQC9dt4uaihgnTKktUlAiUs4KOQJ9BrDa3de4ewq4A7h0UJ+3Ane5+wYAd99ewHiA/BIOjUCLiIwJQyXQa9t48awJxKJlV4koIiWgkN8804GNedctYVu+44AGM/udmT1qZu8Y6o3M7BozW2Zmy1pbW48qqIESji4l0CIiY0L7BsAG1oDe3ZPm2W17tHydiBRNIRPooaZFD147LgacBvwV8Grg02Z23H4Pud/i7ovdfXFzc/NRBVVfFccMdqmEQ0RkbGjfCHV714Be09oJwIlT64oZlYiUsYLVQBOMOM/Mu54BbB6izw537wK6zOwPwCnAc4UKKhox6irjKuEQERkrBi1ht3ZHFwBzJ1YXKyIRKXOFHIFeChxrZnPNLAFcAdw7qM89wEvNLGZmSeBMYFUBYwKCtaA1iVBEZIwYlECv29FFxGBWozZQEZHiKNgItLtnzOw64D4gCtzq7k+b2bXh/ZvdfZWZ/RJ4AsgB33b3pwoVU7/+3QhFRKTEZTP7rQG9ZkcXMxqSJGKaQCgixVHIEg7cfQmwZFDbzYOurweuL2QcgzUk47R29o3mR4qIyJHo2ASe3XcEemcXc1S+ISJFVJb/fG9IJmjrUgmHiJSnQ21yZWb1ZvZTM1sRbnJ19XCfHXGDlrBzd9a2djFPCbSIFFFZJtAq4RCRcpW3ydXFwALgSjNbMKjb+4GV7n4K8HLga2aWGOazI2tQAt3a2UdXKsucJtU/i0jxlGUC3ZCM05XKksrkih2KiMhoG84mVw7UmpkBNcAuIDPMZ0fWwBrQMwBY2xquwNFcU9CPFRE5mPJMoKu1G6GIlK3hbHJ1I3AiwdKjTwIfcvfcMJ8d0c2v2L0RaqcOrAG9bmeYQDephENEiqc8E+j+3Qi1lJ2IlJ/hbHL1amA5MA1YBNxoZnXDfHZEN7+iqxVqJg1crtnRRTxqTG+oOrr3FRE5CmWaQMcB2KXtvEWk/Axnk6urgbs8sBpYC5wwzGdHVqobEntHm9ft6GJWY5JoZKhcXkRkdJRlAj0hqRIOERnbzOw6M2s4gkeHs8nVBuCC8HMmA8cDa4b57MhKd0F874TBtTu6mDtR9c8iUlxlmUA3VAcj0CrhEJExbAqw1Mx+FC4tN6whWXfPAP2bXK0CftS/yVX/RlfAF4BzzOxJ4DfAP7r7jgM9O8I/175SXZAIEuhczlm/s5u5E7UCh4gUV0E3UilVe2ugNQItImOTu3/KzD4NXEhQcnGjmf0I+I67v3CIZw+6yZW7bw7fd1jPFlSqG+JBCRlE6aEAACAASURBVMeWjl76MjmNQItI0ZXlCHRlPEplPKISDhEZ09zdga3hkQEagDvN7F+LGthISu8dge5fwm6ORqBFpMjKcgQawt0IVcIhImOUmX0QeCewA/g28FF3T5tZBHge+Fgx4xsxqe6BGui14RJ28zQCLSJFVrYJtHYjFJExbiLwBndfn9/o7jkze22RYhpZuSxk+wZW4Vjb2kVVPMrkuooiByYi5a4sSzggWMpOI9AiMoYtIdghEAAzqzWzMwHcfVXRohpJqWDEuX8Eet3OLuZMrGaY8yVFRAqmfBPo6oQmEYrIWPYfQGfedVfYNn6ku4PXsAZ63Y4urcAhIiWhfBPoZJx2jUCLyNhl4SRCICjdYLyV5Q2MQAclHNv39DG5rrKIAYmIBMo4gQ5qoHO5/XahFREZC9aY2QfNLB4eHyLY7GT8yBuBTmVydPZlBpYhFREpprJNoCckE+QcOno1Ci0iY9K1wDnAJoItts8ErilqRCMtFSbQ8Wrae4KSu4ZkvIgBiYgExtev+w5D/5dwW3d6YGtvEZGxwt23E2ylPX6lwxKORJK2rmCwo6Fa39ciUnzDSqDN7H/c/e2HahtL8ncjnEt1kaMRETk8ZlYJvAc4CRgoDHb3dxctqJE2MAKdHJj0rRIOESkFwy3hOCn/wsyiwGkjH87omRCOQGstaBEZo/4HmAK8Gvg9MAPYU9SIRtpADXT1wHf1BJVwiEgJOGgCbWafMLM9wIvMrCM89gDbgXtGJcICaQx/DbizUwm0iIxJx7j7p4Eud/8+8FfAyUWOaWTlrQPdv26/RqBFpBQcNIF29y+5ey1wvbvXhUetuze5+ydGKcaC6F8KaXN7b5EjERE5Iv0zoNvNbCFQD8wpXjgFkLcKh0o4RKSUDLeE42dmVg1gZleZ2b+Z2ewCxlVwleF2sBvbuosdiojIkbjFzBqATwH3AiuBrxQ3pBGWvwpHd5qKWISqRLS4MYmIMPwE+j+AbjM7BfgYsB7474JFNUpmNiTZuEsJtIiMLWYWATrcvc3d/+Du89x9krv/Z7FjG1HpLojEIJZgV1dqoPRORKTYhptAZ8Idry4Fvu7uXwdqCxfW6JjZmKSlrafYYYiIHJZw18Hrih1HwaW6B3YhbO9OaclRESkZw02g95jZJ4C3Az8PV+EY81OhZzRUsWV3D+lsrtihiIgcrl+Z2UfMbKaZNfYfxQ5qRKW7IJEEgjX7tYmKiJSK4SbQbwH6gHe7+1ZgOnD9oR4ys4vM7FkzW21mHx/i/svNbLeZLQ+PzxxW9EdpZkOSnMMWTSQUkbHn3cD7gT8Aj4bHsqJGNNJS3RDvT6BTmkAoIiVjWBupuPtWM7sNON3MXgs84u4HrYEOR6lvAl5FsM3sUjO7191XDur6oLu/9ghiP2ozGqsA2NjWzaymZDFCEBE5Iu4+t9gxFFy6e2AEur07rTWgRaRkDHcnwjcTjDj/DjDgm2b2UXe/8yCPnQGsdvc14XvcQVBDPTiBLpqZDcEXsyYSishYY2bvGKr9UIMbY0qqC+LV5HJOu0agRaSEDCuBBv4fcLq7bwcws2bg18DBEujpwMa86xbgzCH6nW1mK4DNwEfc/enBHczsGuAagFmzZg0z5EObWl9JNGKaSCgiY9HpeeeVwAXAY4yDFZIGpLuhsp6O3jQ5hwatwiEiJWK4CXSkP3kO7eTQ9dM2RJsPun4MmO3unWb2GuAnwLH7PeR+C3ALwOLFiwe/xxGLRSNMra/UWtAiMua4+wfyr82snmB77/Ej1Q21U/N2IVQJh4iUhuFOIvylmd1nZu8ys3cBPweWHOKZFmBm3vUMglHmAe7e4e6d4fkSIG5mE4cZ04jQWtAiMk50M8QAxJiW7oJEtXYhFJGSc9ARaDM7Bpjs7h81szcA5xKMLD8M3HaI914KHGtmc4FNwBXAWwe9/xRgm7u7mZ1BkNDvPKKf5AjNbKzigWdbR/MjRUSOmpn9lL2/1YsAC4AfDfPZi4CvA1Hg2+7+5UH3Pwq8LbyMAScCze6+y8zWAXuALMEeAYuP8kc5sFQ3JKppDxNoTSIUkVJxqBKOG4BPArj7XcBdAGa2OLx3yYEedPeMmV0H3EfwJX2ruz9tZteG928GLgfeZ2YZoAe4ItywZdTMbEjSuqeP3nSWyri2iBWRMeOreecZYL27txzqoeGskOTu1xMuVWpmlwB/7+678t7mfHffMQI/w8Glg2XsdnX1l3BoBFpESsOhEug57v7E4EZ3X2Zmcw715mFZxpJBbTfnnd8I3DisSAtkZmOwEkdLWw/HTKopZigiIodjA7DF3XsBzKzKzOa4+7pDPHe4KyRdCdw+MiEfhlwuXMZu7wi0EmgRKRWHqoGuPMi9qpEMpFhmNOxdC1pEZAz5PyB/G9Vs2HYoQ62QNH2ojmaWBC4CfpzX7MD9ZvZouEJSYWTC1ZHiSdq6U0QjRm3lcOe9i4gU1qES6KVm9teDG83sPQS7Xo15AyPQmkgoImNLzN1T/Rfh+XCGaIezQlK/S4A/DSrfeIm7nwpcDLzfzF623weYXWNmy8xsWWvrEc4xSYXfyYlq2rrTTKiKE4kMFbqIyOg71D/n/w6428zext6EeTHBl/TrCxnYaGmuqSARi7BRa0GLyNjSamavc/d7AczsUmA4dcmHXCEpzxUMKt9w983h63Yzu5ugJOQPg/oc/dKj6a7gNZ6kvTulCYQiUlIOmkC7+zbgHDM7H1gYNv/c3X9b8MhGSSRizGio0lJ2IjLWXAvcZmb980hagCF3JxzkkCskwcC60ucBV+W1VRPsC7AnPL8Q+Kej+ikOZGAEOklbV1r1zyJSUoZVUObuDwAPFDiWopnZkNRuhCIyprj7C8BZZlYDmLvvGeZzw1khCYLfMt7v7l15j08m+K0kBH9//NDdfzkyP9Eg6TCBjgfrQM9oSBbkY0REjoRmZBBMJFzR0l7sMEREhs3M/gX4V3dvD68bgH9w908d6tlDrZAUXn8P+N6gtjXAKUcV+HClwrw9kaStu5eTp9ePyseKiAzHcHciHNdmNiZp706zpzdd7FBERIbr4v7kGcDd24DXFDGekRWOQHs8SVt3msZqlXCISOlQAg3MDlfiWLdDddAiMmZEzayi/8LMqoCKg/QfW8IR6F6rIJXJMUE10CJSQpRAAwum1QHw1ObdRY5ERGTYfgD8xszeEy4t+ivg+0WOaeSEI9C7M0Hi3KBVOESkhKgGGpjVmKS2MsZTm5RAi8jY4O7/amZPAK8kWNv5l8Ds4kY1gsJVONrSQeKsEWgRKSUagQbMjIXT6pVAi8hYs5VgN8I3AhcAq4obzggK14HemQ7GeTQCLSKlRCPQoYXT6/j+w+tJZ3PEo/p3hYiUJjM7jmDt5iuBncD/Eixjd35RAxtpqW6wCLt6g90HNYlQREqJMsXQwun1pDI5nt/WWexQREQO5hmC0eZL3P1cd/8mkC1yTCMv3Q3xatp7gtWRVMIhIqVECXSof41RlXGISIl7I0HpxgNm9l9mdgFBDfT4kuoa2IUQ0FbeIlJSlECH5jRVU1MR00ocIlLS3P1ud38LcALwO+Dvgclm9h9mdmFRgxtJ6W6IJ2nrTlFbEVNpnYiUFH0jhSIRY8G0Op7UCLSIjAHu3uXut7n7a4EZwHLg40UOa+SkuiERbOM9oVqjzyJSWpRA5zl5ej2rtnSQyeaKHYqIyLC5+y53/093f0WxYxkx6S6IJ9nTm6GuUgm0iJQWJdB5Fk6vozed44XWrmKHIiJS3lLdkEjSm85SFY8WOxoRkX0ogc7TP5FQZRwiIkUWrsLRk85SlVACLSKlRQl0nrkTa0gmolqJQ0Sk2MJVOHpSWSo1Ai0iJUYJdJ5oxFgwtU4JtIhIsYWrcPRlckqgRaTkKIEe5OQZ9Ty9uYNURhMJRUSKJlyFoyeVpSquv6pEpLToW2mQc+ZPpCedZdn6XcUORUSkPLkPrMLRo0mEIlKClEAPcvb8JuJR4/fPtRY7FBGR8pTpA88FNdBp1UCLSOlRAj1ITUWMxbMb+f2zSqBFRIoiFSwlmoslSakGWkRKkBLoIZx3fDPPbN3Dto7eYociIlJ+0kECnY5WAWgZOxEpOQVNoM3sIjN71sxWm9kBt5g1s9PNLGtmlxcynuE677hmAJVxiIgUQ6o7eIlUAqgGWkRKTsESaDOLAjcBFwMLgCvNbMEB+n0FuK9QsRyuE6bUMqm2Qgm0iEgxhCPQqUgwAl2pVThEpMQU8lvpDGC1u69x9xRwB3DpEP0+APwY2F7AWA6LmXHecc388fkdZLJazk5EZFSFI9C9VACoBlpESk4hE+jpwMa865awbYCZTQdeD9x8sDcys2vMbJmZLWttHZ1R4fOOb2Z3T5oVLe2j8nkiIhJKhwm0qYRDREpTIRNoG6LNB13fAPyju2cP9kbufou7L3b3xc3NzSMW4MGce8xEIoZW4xCRcedQ81PM7KNmtjw8ngrnqDQO59kREa7C0aMRaBEpUYVMoFuAmXnXM4DNg/osBu4ws3XA5cC3zOyyAsY0bBOSCV48q4H7V24rdigiIiNmOPNT3P16d1/k7ouATwC/d/ddw53bctTCEejuMIHWKhwiUmoKmUAvBY41s7lmlgCuAO7N7+Duc919jrvPAe4E/tbdf1LAmA7LpYum8czWPTy1aXexQxERGSnDnZ/S70rg9iN89siENdDduTCB1gi0iJSYgiXQ7p4BriNYXWMV8CN3f9rMrjWzawv1uSPpdadMIxGNcOejLcUORURkpBxyfko/M0sCFxFM9B72s0c9byVchaPTE4BW4RCR0hMr5Ju7+xJgyaC2IScMuvu7ChnLkZiQTPCqkybzk+Wb+MRrTqAiplEQERnzhjM/pd8lwJ/cfdfhPOvutwC3ACxevPhA731gtdNg9rl05/oTaH33ikhp0T/rD+FNp82gvTvNb1eVzCp7IiJHYzjzU/pdwd7yjcN99sid8ha4+uf0ZILcWyUcIlJqlEAfwkuPbWZyXQX/pzIOERkfDjk/BcDM6oHzgHsO99mR0pMOFmjSJEIRKTVKoA8hGjHecOoMfvfsdrZ39BY7HBGRo3IY81NeD9zv7l2HerZQsfaGCXSlyudEpMQogR6GN502g5zDnY9pFFpExj53X+Lux7n7fHf/Yth2c/4cFXf/nrtfMZxnC6UnnSURixCJDFV6LSJSPEqgh2Fecw3nHjORW/+4bmBERERECqs3lVX9s4iUJCXQw3TdK45hR2cfdzyyodihiIiUhd50TkvYiUhJ0jfTMJ01r4kz5jTyn39YQ19Go9AiIoXWk9YItIiUJiXQh+EDFxzDlt29/PjRTcUORURk3OtJZ7UGtIiUJCXQh+HcYyZyyswJfOt3q0lnc8UOR0RkXOtVAi0iJUoJ9GEwMz74imNoaevhf5duPPQDIiJyxHpVwiEiJUoJ9GF6xQmTOGteI9ff9yw7O/uKHY6IyLjVk85qExURKUlKoA+TmfGFSxfS1Zfhy794ptjhiIiMWz1axk5ESpQS6CNw7ORa3vvSefzfoy0sW7er2OGIiIxLvekcFVrGTkRKkL6ZjtAHLziGafWVfOonT2lCoYhIAagGWkRKlRLoI5RMxPjs607ima17+LdfPVfscERExh2tAy0ipUoJ9FF49UlTuOL0mfzH717gD8+1FjscEZFxw921jJ2IlCwl0Efps5ecxHGTa/jwj5azfU9vscMRERkXUtkcOUercIhISVICfZSqElFufOupdPZl+Ls7lqseWkRkBPSmgu9SjUCLSClSAj0Cjptcyz9fdjIPvbCTT//kKdy92CGJiIxpvZksAJVahUNESlCs2AGMF5efNoP1O7v45m9XM6OhiutecWyxQxIRGbN6UkECrUmEIlKKlECPoA+/6jg2tfXw1fufY0p9FZefNqPYIYmIjEk9aSXQIlK6lECPIDPjy298Edv39PGxO1cAKIkWETkC/Ql0pSYRikgJUnHZCEvEIvzXOxZzzvyJfPTOFdzxyIZihyQiMub09ifQMSXQIlJ6lEAXQFUiyrffuZjzjmvm43c9yX/9YY0mFoqIHIb+BFrL2IlIKVICXSCV8Sj/+fbTuHjhFL64ZBWfvFtbfouIDFdPuIydaqBFpBQpgS6giliUm956Kn/78vnc/sgG3vXdR2jrShU7LBGRkjdQwqFl7ESkBBX0m8nMLjKzZ81stZl9fIj7l5rZE2a23MyWmdm5hYynGCIR42MXncDX3nQKS9e28ZpvPMjSdbuKHZaISEnTKhwiUsoKtgqHmUWBm4BXAS3AUjO7191X5nX7DXCvu7uZvQj4EXBCoWIqpjeeNoPjJtfygdsf44pb/szfv/JYrj1vPrGoRldEZHSZ2UXA14Eo8G13//IQfV4O3ADEgR3ufl7Yvg7YA2SBjLsvLkSMvVqFQ+SopNNpWlpa6O3tLXYoY0JlZSUzZswgHo8Pq38hl7E7A1jt7msAzOwO4FJgIIF29868/tXAuJ5pd/KMen76gXP5f3c/xVfvf45frdzGv15+CsdPqS12aCJSJoYzuGFmE4BvARe5+wYzmzTobc539x2FjLNXI9AiR6WlpYXa2lrmzJmDmRU7nJLm7uzcuZOWlhbmzp07rGcKOfw5HdiYd90Stu3DzF5vZs8APwfePdQbmdk1YYnHstbW1oIEO1pqK+N8/YpFfPPKF7OxrYfXfvNB/v1Xzw38ZSEiUmADgxvungL6BzfyvRW4y903ALj79lGOkZ50lmjEiOu3dCJHpLe3l6amJiXPw2BmNDU1HdZofSG/mYb6X2y/EWZ3v9vdTwAuA74w1Bu5+y3uvtjdFzc3N49wmKPPzLjklGn86u9fxsULp/L13zzPBV/7Pb94couWuxORQhvO4MZxQIOZ/c7MHjWzd+Tdc+D+sP2aQgXZk8pp9FnkKCl5Hr7D/bMqZALdAszMu54BbD5QZ3f/AzDfzCYWMKaS0lRTwTeufDE//Oszqa2M8b7bHuMtt/yZZZpkKCKFM5zBjRhwGvBXwKuBT5vZceG9l7j7qcDFwPvN7GX7fcAI/NawJ52lUgm0iJSoQibQS4FjzWyumSWAK4B78zuY2TEWpvxmdiqQAHYWMKaSdM78ifzsA+fyhcsWsqa1i8tvfpirv/sIKza2Fzs0ERl/hjO40QL80t27wlrnPwCnALj75vB1O3A3QUnIPkbit4Z96ayWsBMZo3bu3MmiRYtYtGgRU6ZMYfr06QPXqdTwlvO9+uqrefbZZwsc6ZEr2CRCd8+Y2XXAfQQzvW9196fN7Nrw/s3AG4F3mFka6AHe4mVawxCLRnj7WbN546nT+f5D67n59y9w6U1/4iXHNPG+847hJceojklERsTA4AawiWBw462D+twD3GhmMYKBjTOBfzezaiDi7nvC8wuBfypEkD3prEo4RMaopqYmli9fDsDnPvc5ampq+MhHPrJPH3fH3YlEhv6H8ne/+92Cx3k0CrkKB+6+BFgyqO3mvPOvAF8pZAxjTTIR430vn89VZ83i9kc28O0H13LVd/7CCVNqedc5c7h00XRtbSsiR2w4gxvuvsrMfgk8AeQIlrp7yszmAXeH/5iPAT90918WIs6edFbfdSIj5PM/fZqVmztG9D0XTKvjs5ecdFjPrF69mssuu4xzzz2Xv/zlL/zsZz/j85//PI899hg9PT285S1v4TOf+QwA5557LjfeeCMLFy5k4sSJXHvttfziF78gmUxyzz33MGnS4MWBRpd+P1aiaivjXPOy+fzhY+fzlTeejJnx8bue5Kwv/YbP3fs0z2wd2f8QRKR8uPsSdz/O3ee7+xfDtpsHDXBc7+4L3H2hu98Qtq1x91PC46T+ZwuhN52lMqYEWmS8WblyJe95z3t4/PHHmT59Ol/+8pdZtmwZK1as4Fe/+hUrV67c75ndu3dz3nnnsWLFCs4++2xuvfXWIkS+r4KOQMvRq4xHecvps3jz4pk8snYX//Pn9fzwLxv43kPreNGMei5bNJ1LTplGc21FsUMVERkxPekc9VXD29BARA7ucEeKC2n+/PmcfvrpA9e333473/nOd8hkMmzevJmVK1eyYMGCfZ6pqqri4osvBuC0007jwQcfHNWYh6IEeowwM86c18SZ85po60px1+ObuOuxFv7pZyv54pJVnDO/iYsXTuXCkyYzsUbJtIiMbb2pLFPq9F0mMt5UV1cPnD///PN8/etf55FHHmHChAlcddVVQ67FnEgkBs6j0SiZTGZUYj0YJdBjUEN1gvecO5f3nDuX57ft4SfLN/HzJ7bwybuf5FM/eZLFsxt55YJJXHDiZOZNrNbkQxEZc3ozmkQoMt51dHRQW1tLXV0dW7Zs4b777uOiiy4qdljDogR6jDt2ci0fffUJfOTC43lm6x5+8eQWfrVqO/+y5Bn+ZckzzGys4rzjmnnZsc2cOa9JvxIVkTGhJ6V1oEXGu1NPPZUFCxawcOFC5s2bx0te8pJihzRsNtZWjVu8eLEvW7as2GGUvJa2bh54Zju/f66Vh17YSXcqS8Tg5BkTOHteE2fObeS0OQ3UVSqhFhlNZvaouy8udhyj5Ui/s0/+3H288dQZfO51pVO7KTKWrFq1ihNPPLHYYYwpQ/2ZHeg7WyPQ49SMhiRvP3sObz97DqlMjsc2tPHQCzt5aPUOvvPHNdz8+xcwgxOm1HHqrAmcNruBRTMnMKepmkhEJR8iUly9WsZOREqYEugykIhFOGteE2fNa+LDrzqOnlSWxze28cjaXTy6vo17l2/mtr9sAKCuMsaLZkxg4fR6Tp5ez0nT6pjVmFRSLSKjJpPNkc66lrETkZKlBLoMVSWinDN/IufMnwhANues3t7Jio3tLG9pZ8XGdr7zxzWks0F5T3UiyglT6zhxai3HT67luPBoqE4c7GNERI5IbyYHQFVCWxWISGlSAi1EI8bxU2o5fkotbz59JgB9mSzPb+vkqU27WbWlg5VbOrhn+Wb29O5dOmZiTYL5zTXMn1TD/OYa5jVXM7epmhkNVcSi+otPRI5MTyoLoFU4RKRkKYGWIVXEoiycXs/C6fUDbe7O1o5entm6h9XbOlm9vZPntu/h509sYXdPeqBfLGLMakwyqynJ7MYks5qqmdlQxczGJDMaqqjVxEUROYjedJBAVyiBFpESpQRahs3MmFpfxdT6Ks4/fu8e9O7Orq4Ua3Z0sXZHF+t2dLFuZxfrd3bz6Lo29vTtu+B5fVWcGQ1VTJtQxfQJVUytr2TqhCqm1Vcypb6SSbWVJGIawRYpV/0JtEagRaRUKYGWo2ZmNNVU0FRTwelzGve55+60d6fZ2NbNhl3dtLT1sKmth5a2bjbs7ObhF3bS2bf/jkITaxJMqq1kcl0Fk+sqmVRbQXNdJc01FTTXVtBcU8HE2gTJhP4vLDLe9CiBFhnzXv7yl/OJT3yCV7/61QNtN9xwA8899xzf+ta3hnympqaGzs5ONm/ezAc/+EHuvPPOId/3q1/9KosXH3g10BtuuIFrrrmGZDJ59D/IASj7kIIyMxqqEzRUJ3jRjAlD9unoTbN1dy9bdveydXcPW3f3sbWjh20dfWzf08uTmzrY2dXHUEuWJxNRJtZU0FSToKm6gqbqBI01CRqTCRqrg6OhOkFDMs6EZIK6yph2ZhQpcb3p/kmESqBFxqorr7ySO+64Y58E+o477uD6668/5LPTpk0bMnkerhtuuIGrrrpKCbSMb3WVceoq4xw3ufaAfTLZHLu6Umzf00drZx+te/rY0dnHjj0pdnYF5y1t3axoaaetK0UmN/QGQdGIMaEqzoQwoZ5QFae+Kk59Mnitqwxfq/pfY9RVxqmtjFGdiGk5P5FR0D8CXRlXKZfIiPjFx2HrkyP7nlNOhou/fMDbl19+OZ/61Kfo6+ujoqKCdevWsXnzZhYtWsQFF1xAW1sb6XSaf/7nf+bSSy/d59l169bx2te+lqeeeoqenh6uvvpqVq5cyYknnkhPT89Av/e9730sXbqUnp4eLr/8cj7/+c/zjW98g82bN3P++eczceJEHnjgAe6//34++9nP0tfXx/z58/nud79LTU3NUf34SqBlTIhFI0yqq2RSXeUh+7o7e/oy7OpM0dYdHLu60rR391+n2d2dpr0nxZbdwaTI3T3pIUtJ8plBTcXehLq2Msb/b+/uY6yq7zyOv7/3YR4YBHkIddaRglFXSjs8dEKhGouybKG7inH/gCmm9aEhJW5w1822ahOTTTBZU8PaRqWhLkt2aySmq60xlbba2SUbiwKuneVBWrb4MEhhhlieZ+Y+fPePc+5wYWdg7sxcDufM55XcnN/vdx74fe8MX76ce+45Y2szjK3LMrY201dkj63LMLY2TUNthobaYJsxNelwGfTrsimdCRcZQOkuHHqUt0h8TZo0iXnz5rFlyxaWLVvG5s2bWb58OfX19bz88suMGzeOrq4u5s+fzx133DHgv4nr169nzJgxtLe3097ezty5c/vWPf7440ycOJFCocCiRYtob29nzZo1rFu3jra2NiZPnkxXVxdr167l9ddfp6GhgSeeeIJ169bx2GOPDSs+FdCSOGbWd1Z7Gg2D3i9fKHKiO8+xMzmOd+c4fiZon+jOcaI7z/Hzlqd68nSd7OX9o6c50Z3nZE+u76Pni88RGmqCwjp4Be36sn59TZr6bLpvvNSuywbt0lhd+KqvSVOXSYXLtM6WS2zpS4QiI+wCZ4qrqXQZR6mA3rhxI+7Oo48+ytatW0mlUhw8eJDDhw9z1VVX9XuMrVu3smbNGgCam5tpbm7uW/fiiy+yYcMG8vk8hw4dYs+ePeesB9i2bRt79uzhpptuAqC3t5cFCxYMOzYV0CKhTDrVd732UOULRU71FDjRk+NUT4GTPUGhfbo3z6meAqdKy548p3sLnMnlOdlT4Exv0D/RnefI8R5O5/Kc6S2E2xT6vf77YmrS9zK43wAAC4xJREFUKWozKWqzaeqyqbDQTlGXCQruYF3Qr82mqM2EY+E+tZkUNWE/WKapSQftmrJ1tZkUNen0OeM16RTZtOksuwxJd05noEWS4M477+Shhx7inXfe4cyZM8ydO5dNmzbR2dnJzp07yWazTJs2je7u7gsep79/Sw4cOMCTTz7J9u3bmTBhAvfcc0+/x3F3Fi9ezAsvvDBicYEKaJERlUmnGD8mxfgxI3eva3enO1ekO1fgdK7Amd5C0A6X3bmgyA7axXPa3bkCPfmgXVqWtv/kdC/duQK9hWKwvqw9UsoL7mzawmWpwB5grLRtOkUmbeesK/Wz4TKTTlFT1s6mjExpu1SwXSbcPpMKxjMp6ztWJtwmXRpLBW0V/tHSXThEkmHs2LEsXLiQ++67j9bWVgCOHTvGlClTyGaztLW18cEHH1zwGLfccgvPP/88t956K7t27aK9vR2A48eP09DQwPjx4zl8+DCvvfYaCxcuBOCKK67gxIkTTJ48mfnz5/PAAw+wf/9+rrvuOk6fPk1HRwc33HDDsGJTAS1ymTOz4HKNmjQTLsGf5+70For05oNiutTuyRfozZfaZctwffAqnNsvOL35IrnSWCFol/q5cP2JXL5vLF/0c9vhfvmiUxjgy6EjrVRIny20g355EX7fzdNZ+YVPX5L5jDZ9BbTuwiESe62trdx1111s3rwZgJUrV3L77bfT0tLC7NmzufHGGy+4/+rVq7n33ntpbm5m9uzZzJs3D4BZs2YxZ84cZs6cybXXXtt3iQbAqlWrWLp0KY2NjbS1tbFp0yZaW1vp6ekBYO3atcMuoM2H8tlwhFpaWnzHjh1RT0NEIlAsel8xncsXyRWL5AseFuVOvlgkl/fzxoN2vhjsVxoP2mVjxSKFgpMLxwtF7ztmvm+Mvv7tzY0s+WxjxTGY2U53H/gGpgkzlJz92v8c4qfvfsz6u+fq0wCRIdq7dy8zZsyIehqx0t97NlDO1hloEYmNVMqoS4VnJWujnYtUz9LPNbL0c5X/50RE5FLRTTZFRERERCqgAlpEREQkgeJ2mW6UKn2vVECLiIiIJExdXR1Hjx5VET0I7s7Ro0epq7v4w9pKqnoNtJktAb4HpIHn3P0fz1u/Evh22D0JrHb331RzTiIio93FcnO4zULgKSALdLn7lwa7r4hEr6mpiY6ODjo7O6OeSizU1dXR1NQ06O2rVkCbWRp4BlgMdADbzewVd99TttkB4Evu/omZLQU2AF+o1pxEREa7weRmM7sSeBZY4u4fmtmUwe4rIpeHbDbL9OnTo55GYlXzEo55wH53/7279wKbgWXlG7j7m+7+SdjdBgy+9BcRkaG4aG4Gvgq85O4fArj7kQr2FRFJvGoW0FcDH5X1O8KxgdwPvFbF+YiIyOBy8w3ABDP7DzPbaWZfq2BfzGyVme0wsx36+FhEkqia10D3d/f7fq9kN7NbCQromwdYvwpYBTB16tSRmp+IyGg0mNycAT4PLALqgV+b2bZB7ou7byC4JI+WlhZ9g0lEEqeaBXQHcE1Zvwn4+PyNzKwZeA5Y6u5H+ztQeTI2s04zu/CD0/s3Gegawn5xofjiTfHFWyXxRf3878Hk5g6CLw6eAk6Z2VZg1iD3PcfOnTu7lLP7pfjiTfHF27BzdtUe5W1mGeC3BGcwDgLbga+6++6ybaYCvwK+5u5vVmUiZ/+sHUl+fK7iizfFF29xim+QuXkG8DTwZaAGeBtYAbx3sX1HcJ6xeU+HQvHFm+KLt5GIr2pnoN09b2Z/Dfyc4HZHG919t5l9M1z/A+AxYBLwrJkB5JP8AxMRidpgcrO77zWzLUA7UCS4Xd0ugP72jSQQEZEIVfU+0O7+M+Bn5439oKz9DeAb1ZyDiIic62K5Oex/F/juYPYVERltRtOTCDdEPYEqU3zxpvjiLenxRSHp76niizfFF2/Djq9q10CLiIiIiCTRaDoDLSIiIiIybCqgRUREREQqkPgC2syWmNk+M9tvZg9HPZ/hMrNrzKzNzPaa2W4zezAcn2hmvzSz34XLCVHPdTjMLG1m/21mr4b9xMRnZlea2Y/N7L3w57ggYfH9bfi7ucvMXjCzujjHZ2YbzeyIme0qGxswHjN7JMw3+8zsy9HMOt6Ut+NHOTvW8SlnDyFnJ7qANrM08AywFPgM0Gpmn4l2VsOWB/7O3WcA84EHwpgeBt5w9+uBN8J+nD0I7C3rJym+7wFb3P1GgodT7CUh8ZnZ1cAaoMXdP0twq7MVxDu+TcCS88b6jSf8u7gCmBnu82yYh2SQlLdjSzk7hpSzh5Gz3T2xL2AB8POy/iPAI1HPa4Rj/CmwGNgHNIZjjcC+qOc2jJiawl/w24BXw7FExAeMAw4QfoG3bDwp8V0NfARMJLhN5qvAn8c9PmAasOtiP6/zcwzB/ZIXRD3/OL2Ut6Of3xDiUc6Ob3zK2UPM2Yk+A83ZX4ySjnAsEcxsGjAHeAv4lLsfAgiXU6Kb2bA9BXyL4AEOJUmJ71qgE/iX8OPO58ysgYTE5+4HgSeBD4FDwDF3/wUJia/MQPEkOudcIol+DxOat5WzYxqfcvbQ803SC2jrZywR9+0zs7HAvwN/4+7Ho57PSDGzvwSOuPvOqOdSJRlgLrDe3ecAp4jXR2MXFF5XtgyYDvwJ0GBmd0c7q0sqsTnnEkrse5jEvK2cHW/K2UPPN0kvoDuAa8r6TcDHEc1lxJhZliAJP+/uL4XDh82sMVzfCByJan7DdBNwh5m9D2wGbjOzH5Gc+DqADnd/K+z/mCA5JyW+PwMOuHunu+eAl4Avkpz4SgaKJ5E55xJL5HuY4LytnB3v+JSzh5hvkl5AbweuN7PpZlZDcKH4KxHPaVjMzIB/Bva6+7qyVa8AXw/bXye4xi523P0Rd29y92kEP69fufvdJCe+PwAfmdmfhkOLgD0kJD6CjwHnm9mY8Hd1EcEXbpISX8lA8bwCrDCzWjObDlwPvB3B/OJMeTtGlLOBGMeHcvbQc3bUF3pfggvJvwL8Fvhf4DtRz2cE4rmZ4OOFduDd8PUVYBLBlzh+Fy4nRj3XEYh1IWe/kJKY+IDZwI7wZ/gTYELC4vsH4D1gF/BvQG2c4wNeILg2MEdwtuL+C8UDfCfMN/uApVHPP44v5e14vpSzo5/rEONTzh5CztajvEVEREREKpD0SzhEREREREaUCmgRERERkQqogBYRERERqYAKaBERERGRCqiAFhERERGpgApoSRQzK5jZu2WvEXtilJlNM7NdI3U8EZHRTjlb4ioT9QRERtgZd58d9SRERGRQlLMllnQGWkYFM3vfzJ4ws7fD13Xh+KfN7A0zaw+XU8PxT5nZy2b2m/D1xfBQaTP7oZntNrNfmFl9uP0aM9sTHmdzRGGKiCSCcrZc7lRAS9LUn/dx4PKydcfdfR7wNPBUOPY08K/u3gw8D3w/HP8+8J/uPguYC+wOx68HnnH3mcAfgb8Kxx8G5oTH+Wa1ghMRSRjlbIklPYlQEsXMTrr72H7G3wduc/ffm1kW+IO7TzKzLqDR3XPh+CF3n2xmnUCTu/eUHWMa8Et3vz7sfxvIuvtaM9sCnCR4zOtP3P1klUMVEYk95WyJK52BltHEB2gPtE1/esraBc5+j+AvgGeAzwM7zUzfLxARGR7lbLlsqYCW0WR52fLXYftNYEXYXgn8V9h+A1gNYGZpMxs30EHNLAVc4+5twLeAK4H/d0ZFREQqopwtly39j0uSpt7M3i3rb3H30m2Ras3sLYL/OLaGY2uAjWb290AncG84/iCwwczuJzhrsRo4NMCfmQZ+ZGbjAQP+yd3/OGIRiYgkl3K2xJKugZZRIbyersXdu6Kei4iIXJhytlzudAmHiIiIiEgFdAZaRERERKQCOgMtIiIiIlIBFdAiIiIiIhVQAS0iIiIiUgEV0CIiIiIiFVABLSIiIiJSgf8D7AN2C6FmNqAAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_cost_accuracy(costs, train_accs, val_accs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "X_plot, y_plot = X_test[:1000, :].copy(), y_test[:1000].copy()\n",
    "X_plot, y_plot = X_plot.T, y_plot.reshape((1, y_plot.shape[0]))\n",
    "plot_decision_boundary(lambda x: model_trained(x), X_plot, y_plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<tf.Tensor: shape=(), dtype=float32, numpy=0.9212>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_data = tf.data.Dataset.from_tensor_slices((X_test, y_test)).batch(32)\n",
    "test_accuracy = tf.keras.metrics.SparseCategoricalAccuracy(name='test_accuracy')\n",
    "for x_, y_ in val_data:\n",
    "    y_pred = model_trained(x_)\n",
    "    test_accuracy(y_, y_pred)\n",
    "test_accuracy.result()"
   ]
  }
 ],
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